DESCRIPTION: The overall aim of this project is to improve our understanding of the three dimensional structure prediction of oligopeptides, polypeptides, and proteins. Based upon his recent advances in: (a) decomposition-based global optimization approach, GOP, (b) branch and bound with difference of convex functions transformation global optimization method, alphaBB, (c) the application of GOP to the structure prediction of Lennard-Jones Clusters, (d) the application of alphaBB to small acyclic molecules, (e) the application of the alphaBB coupled with ECEPP/3 to the naturally occurring amino acids and small oligopeptides, (f) the application of alphaBB coupled with ECEPP/3 to Met-enkephalin and its comparison to simulated annealing and other search methods, (g) the application of alphaBB to Decaglycine, and (h) the computational complexity that he has observed on the considered systems, the proposed research work is directed at determining (i) the global minimum potential energy conformation of peptides and proteins, (ii) low energy conformations of peptides close to the global minimum one, and (iii) saddle points of the potential energy hypersurface of the peptides that are close to the global minimum peptide structure. Dr. Floudas plans to focus on the following objectives: (a) Study the deterministic global optimization approach described in the preliminary studies section 3.3 for (i) oligopeptides such as Leu- enkephalin, Gramicidin S, and Mellitin, (ii) the incorporation of solvation effects and the application to the pentapeptides of Met- enkephalin and Leu-enkephalin in the presence of water, (iii) calculations of the relative entropy and relative free energy, and (iv) constrained peptide systems. (b) Investigate modifications of the global optimization method in (a) so as to be extended to handle polypeptide molecules such as the avian pancreatic polypeptide, models of fibrous proteins such as collagen,a nd globular proteins such as bovine trypsin inhibitor. (c) Study the topography of the total potential energy surface by developing and applying a new method for the determination of saddle points of the peptide energy hyersurface that are close to the global minimum one. (d) Develop distributed computing algorithms for global optimization methods for (a), (b), and (c), apply them to olgopeptides, polypeptides, proteins, and develop tools for the prediction of three-dimensional structures of peptides.